Method and a Computer System for Forecasting the Value of a Structured Financial Product

ABSTRACT

A method and system for forecasting the value of a structured financial product, which can be a weather-based structured financial product. The method and system calculate a forecast value based on forecasted weather data for a defined time period in a defined geographical area, calculate reference weather data from historical data for the defined time, and the defined geographical area, and calculate a quality indicator, indicative of a forecasting quality associated with the forecasted weather data, based on the forecasted weather data and the reference weather data.

FIELD OF THE INVENTION

The present invention relates to a method and a computer system forforecasting the value of a structured financial product. Specifically,the present invention relates to a computer implemented method and acomputer system for forecasting the value of a weather-based structuredfinancial product.

BACKGROUND OF THE INVENTION

In the insurance industry, insurance policies are provided for insuringagainst the occurrence of specific weather conditions. Likewiseinsurance companies and financial services companies offer financialinstruments, particularly financial derivatives, generally referred toas structured financial products, that are based on weather conditions.Weather-based structured financial products are investment vehicleswhose values are based on specified weather measures, such astemperature, precipitation, hours of sunshine, heating degree days,cooling degree days or wind speed.

U.S. Pat. No. 6,418,417 describes a method for valuating weather-basedfinancial instruments. For a financial instrument having a start dateand a maturity date, and being defined for a particular geographicregion and at least one weather condition, a value of the weather-basedfinancial instrument is determined based on historical weather data andfuture weather data, forecasted for the period between start date andmaturity date.

U.S. Pat. No. 2004/0230519 describes a method of generating a pricingmodel for weather derivatives. The pricing model is based on historicalweather data and current weather data. The model utilizes deviations ofthe current weather data from the historical weather data or frompredicted measures.

SUMMARY OF THE INVENTION

It is an object of this invention to provide a computer implementedmethod and a computer system for forecasting the value of aweather-based structured financial product based on forecasted weatherdata. It is a further object of the present invention to provide acomputer implemented method and a computer system for forecasting thevalue of a weather-based structured financial product on the basis offorecasted weather data, wherein the quality of the forecasted weatherdata is considered.

According to the present invention, these objects are achievedparticularly through the features of the independent claims. Inaddition, further advantageous embodiments follow from the dependentclaims and the description.

According to the present invention, the above-mentioned objects areparticularly achieved in that, for forecasting a value of aweather-based structured financial product, a forecast value iscalculated based on forecasted weather data for a defined time periodand a defined geographical area. Reference weather data is calculatedfrom historical weather data for the defined time period and the definedgeographical area. Based on the forecasted weather data and thereference weather data, calculated is a quality indicator, indicative ofa forecasting quality associated with the forecasted weather data.Calculating reference weather data from the historical weather data andcalculating a forecasting quality indicator from the reference weatherdata and the forecasted weather data make it possible to provide aquality measure for the forecasted value of the financial product. Thequality measure enables both investors and providers of the financialproduct to make better-informed decisions concerning the value of thefinancial product.

In a preferred embodiment, a reference value is calculated based on thereference weather data, and the value of the financial product iscalculated from the reference value and from the forecast value weightedby the quality indicator. Calculating the value of the financial productfrom the reference value and from the forecast value, and weighting theforecast value with the quality indicator make it possible to adjust theinfluence of the forecast value on the calculated value of the financialproduct. The influence of the forecast value depends on the quality ofthe forecasted weather data. Preferably, the weight of the forecastvalue increases with improved accuracy of the forecasted weather dataover the reference weather data. Consequently, the calculated value ofthe financial product has an improved probability of being accurate. Inaddition the presented innovation helps to create and steer an optimalweather derivative portfolio.

Preferably, a forecasted weather index is determined from the forecastedweather data, and the forecast value is calculated by applyingstructural parameters of the financial product to the forecasted weatherindex. Likewise, a reference weather index is determined from thereference weather data, and the reference value is calculated byapplying the structural parameters of the financial product to thereference weather index. For example, the forecasted weather data, thereference weather data, and the historical weather data includetemperature data. Correspondingly, the forecasted weather index and thereference weather index include at least one of average temperature,cumulative temperature, number of heating degree days, and number ofcooling degree days for the defined time period and the definedgeographical area.

In an embodiment, calculating the quality indicator includes calculatinga ranked probability score for the forecasted weather data, calculatinga ranked probability score for the reference weather data, andcalculating the quality indicator as a ranked probability skill scorefrom the ranked probability score for the forecasted weather data andthe ranked probability score for the reference weather data.

In an embodiment, the forecasted weather data is calculated frommulti-year historical weather data and from long-term weather forecastdata covering one or more months.

In a further preferred embodiment, calculating the forecasted weatherdata includes determining for the defined time period a first cumulateddistribution function for the historical weather data, calculatingcumulative values included in terciles of the first cumulateddistribution function, and determining for the defined time period asecond cumulated distribution function for the forecasted weather data.The second cumulated distribution function is obtained by downscalingthe first cumulated distribution function using quantile levels,obtained from the long term weather forecast data, for the cumulativevalues included in the terciles of the first cumulated distributionfunction.

In an alternative embodiment, calculating the forecasted weather dataincludes determining for the defined time period a reference climatologycomprising deterministic components from the historical weather data,and calculating the forecasted weather data from the referenceclimatology and from ensemble forecasts for the defined time period.

In an embodiment, multiple sets of forecasted weather data forsubsequent time periods are stored assigned to their respective timeperiod. The forecasted weather data is calculated from the multiple setsof forecasted weather data, each set of forecasted weather data beingweighted by a weighting factor having a value increasing from one timeperiod to a subsequent time period. Including weighted sets offorecasted weather data from previous time periods makes it possible toimprove the quality (i.e. accuracy) of the forecasted weather data.

In an embodiment, the forecasted weather data includes a firstcumulative distribution function of temperature data determined frommulti-year historical temperature data and from long term temperatureforecast data, covering one or more months. Calculating the referenceweather data includes determining a second cumulative distributionfunction of temperature data by applying a stochastic time series modelto the historical temperature data. The forecast value is calculated byapplying structural parameters of the financial product to a forecastedweather index determined from the first cumulative distributionfunction. The reference value is calculated by applying structuralparameters of the financial product to a reference weather indexdetermined from the second cumulative distribution function. Calculatingthe quality indicator includes calculating a first ranked probabilityscore based on the first cumulative distribution function, calculating asecond ranked probability score based on the second cumulativedistribution function, and calculating the quality indicator as a rankedprobability skill score from the first ranked probability score and thesecond ranked probability score.

In addition to a computer implemented method and a computer system forforecasting the value of a weather-based structured financial product,the present invention also relates to a computer program productincluding computer program code means for controlling one or moreprocessors of a computer, such that the computer executes the method offorecasting the value of a weather-based structured financial product.Particularly, the computer program product includes a computer readablemedium containing therein the computer program code means.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be explained in more detail, by way ofexample, with reference to the drawings in which:

FIG. 1 shows a block diagram illustrating schematically an exemplaryconfiguration of a computer system for practicing embodiments of thepresent invention, said configuration comprising a computer with adisplay and data entry means.

FIG. 2 shows a flow diagram illustrating an example of a sequence ofsteps executed according to the present invention for forecasting avalue of a weather-based structured financial product.

FIG. 3 shows a block diagram illustrating schematically a stochastictime series model for generating reference weather data based onhistorical weather data.

FIG. 4 shows a block diagram illustrating schematically a daily anomalymethod for generating forecasted weather data, based on historicalweather data and long-term weather forecast data.

FIG. 5 a shows a cumulative distribution function for historical weatherdata, cumulative values being indicated for terciles of the distributionfunction.

FIG. 5 b shows a cumulative distribution function of forecasted weatherdata downscaled based on the cumulative distribution function shown inFIG. 5 a.

FIG. 6 a shows a cumulative distribution function illustrating thecalculation of a ranked probability score for forecasted weather data,determined according to the daily anomaly method.

FIG. 6 b shows a cumulative distribution function illustrating thecalculation of a ranked probability score for forecasted weather data,determined according to the tercile method.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In FIG. 1, reference numeral 1 refers to a computer system. The computersystem 1 includes one or more computers 1′, for example personalcomputers, comprising one or more processors. As is illustratedschematically, the computer system 1 includes a display and data entrymeans, for example a keyboard 17 and a pointing device 2 in the form ofa computer mouse. The computer system 1 further includes memory, adatabase 16, and various functional modules, namely a weather referencemodule 11, a weather forecast module 12 with a weighting module 121, areference module 13, a value forecasting module 14 with a weightingmodule 141, and a quality indicator module 15. Preferably, thefunctional modules and the database 16 are implemented as programmedsoftware modules. The computer program code of the software modules isimplemented as a computer program product, preferably stored on acomputer readable medium, either in memory integrated in a computer 1′of the computer system 1 or on a data carrier that can be inserted intoa computer 1′ of the computer system 1. In a variant, the computersystem 1 is connected to an external data source 5, for example via atelecommunications network.

The weather reference module 11 is configured to establish referenceweather data, based on the historical weather data. The historicalweather data is stored in database 16 or retrieved from external datasource 5. As is illustrated in FIG. 3, the historical weather data,illustrated in block 31 as a time series covering many years, isdecomposed in portions with deterministic data, illustrated in blocks 32and 33, and a portion with stochastic data, illustrated in block 34. Thedeterministic portions include historical trend data, illustrated inblock 32, as well as seasonal pattern data, illustrated in block 33. Thereference weather data is determined for a defined time period and adefined geographical area. The time period and the geographical area aredefined in correspondence with the parameters of the structuredfinancial product to be forecasted. The reference weather data,illustrated in block 38, is simulated through application of astochastic time series model to the historical weather data.Specifically, the reference weather data is established from thedeterministic data, applicable to the defined time period, through autoregression, and from stochastic data determined for the time period. Noforecasted weather data is used in determining the reference weatherdata.

The weather forecast module 12 is configured to establish forecastedweather data, based on multi-year historical weather data and long-termweather forecast data covering one or more months. The historicalweather data and the long-term weather forecast data are provided, forexample, by a forecasting service provider such as the European Centerfor Medium range Weather Forecasting (ECMWF) for the defined time periodand geographical area. The long-term weather forecast data is stored indatabase 16 or retrieved from external data source 5.

In a first variant, the forecasted weather data is determined using adaily anomaly method illustrated in FIG. 4. In the daily anomaly method,the long-term weather forecast data is provided in the form of so calledensemble forecasts (anomalies), illustrated in block 41. The ensembleforecasts are combined with reference climatology data, illustrated inblocks 42 and 43. The reference climatology data represents a specifiednumber of years of historical data and includes historical trend data,illustrated in block 43, and seasonal pattern data, illustrated in block42, for example. The forecasted weather data, illustrated in block 44,is thus generated through recomposition of deterministic weather dataand a number (ensemble) of possible forecasts for the defined timeperiod.

In a second, preferred variant, the forecasted weather data isdetermined using a tercile method illustrated in FIGS. 5 a and 5 b. Asis illustrated in FIG. 5 a, for the historical weather data, acumulative distribution function 50 is determined for the defined timeperiod, for example, the cumulative distribution of the (daily)temperature in the defined time period. For each tercile 51, 52, 53(quantile of one third) of the historical weather data's cumulativedistribution function 50, calculated are the cumulative values (e.g.11850, 12100 and 13600) included in the tercile 51, 52, 53. As isillustrated in FIG. 5 b, for the forecasted weather data, a cumulativedistribution function 57 is determined for the defined time period. Fromthe long-term weather forecast data, determined are quantile levels(e.g. 5%, 12% and 83%) corresponding to the cumulative values (e.g.11850, 12100 and 13600) included in the terciles 51, 52, 53 of thecumulative distribution function 50 of the historical weather data. Thecumulated distribution function 57 for the forecasted weather data isestablished by downscaling the cumulated distribution function 50 of thehistorical weather data, using the quantile levels (e.g. 5%, 12% and83%) determined from the long term weather forecast data.

Multiple sets of forecasted weather data for subsequent time periods arestored in database 16 assigned to their respective time period. Theweighting module 121 is configured to calculate forecasted weather data,to be used for further computation, from the multiple sets of forecastedweather data stored in database 16. Each set of forecasted weather datais weighted by a weighting factor having a value that increases from onetime period to the next subsequent time period. For example, the lengthof a time period is one month and the sets of forecasted weather dataincludes forecasted weather data for the current month and for timeperiods having a lag of one, two, three and four months. For example,the forecasted weather data, to be used for further computation, iscalculated using weighting factors of 60%, 20%, 10%, 7.5% and 2.5% forthe current month or for the time periods having a lag of one, two,three and four months, respectively.

In the following paragraphs, an example of a sequence of steps executedaccording to the present invention for forecasting a value of aweather-based structured financial product is described with referenceto FIG. 2.

In step S1, the value forecasting module 14 calculates a forecastedvalue of a structured financial product. At first, a forecasted weatherindex is calculated from the forecasted weather data calculatedaccording to the daily anomaly method or tercile method, described abovewith reference to FIGS. 4 or 5 a and 5 b, respectively. For example, thehistorical weather data and consequently the forecasted weather datainclude temperature data, and the forecasted weather index includes theaverage temperature, the cumulative temperature, the number of heatingdegree days, or the number of cooling degree days for the defined timeperiod and the defined geographical area. The type of index is definedby a respective parameter of the financial product to be forecasted. Theforecasted weather index can be calculated from the weather data by theanomaly method illustrated in FIG. 4. Alternatively, the tercile methodshown in FIGS. 5 a and 5 b can be applied to calculate an average orcumulative temperature index. It is possible to extend the tercilemethod to further indices like cooling or heating degree days. This canbe achieved by calculating the conditional distribution of the desiredtemperature index given the forecasted terciles of the averagetemperatures. One skilled in the art will understand that the proposedmethod and system are not limited to temperature data but that weatherdata can also be represented by precipitation quantities, or speeds anddirections of wind, for example. Subsequently, in step S12, theforecasted value of the structured financial product is calculated byapplying structural parameters of the financial product to theforecasted weather index calculated in step S11. For example, forfinancial products having a put or call deal structure, in addition tothe geographical area, the type of weather-based index, and the timeperiod, the structural parameters include parameter values for strike(P_(strike)), tick (P_(tick)) and limit (P_(limit)). For a put or calldeal structure, the forecasted value of the structured financial productis calculated according to formula (1) or (2), respectively.

V _(forecasted-put) =E[min (max((P _(strike) −I _(forecasted))·P_(tick,)0),P _(limit))]  (1)

v _(forecasted-call) =E[min(max((I _(forecasted) −P _(strike))·P_(tick),0),P _(limit))]  (2)

I_(forecasted) represents a random variable which follows the forecastedindex distribution.

In step S13, the forecasted value of the structured financial product isdisplayed as output on display 3.

In step S2, the reference module 13 calculates a reference value of thestructured financial product. At first, a reference weather index iscalculated in step S21 from the reference weather data, calculated asdescribed above with reference to FIG. 3. Corresponding to thehistorical weather data, the reference weather data includes temperaturedata, and the reference weather index includes the average temperature,the cumulative temperature, the number of heating degree days, or thenumber of cooling degree days for the defined time period and thedefined geographical area. The reference weather index is calculatedfrom the reference weather data, as described above for the forecastedweather index. Subsequently, in step S22, the reference value of thestructured financial product is calculated by applying the structuralparameters of the financial product to the reference weather indexcalculated in step S21, as explained above for the forecasted value ofthe financial product.

In step S3, the quality indicator module 15 calculates a qualityindicator that indicates the quality of forecasting the forecastedweather data. The quality indicator is calculated based on theforecasted weather data and the reference weather data. In step S31, aranked probability score (RPS_(forecasted)) is calculated for theforecasted weather data according to formula (3), whereinCDF_(forecasted) is the cumulative distribution function of theforecasted weather data and CDF_(actual) is the cumulative distributionfunction of the actual realization, i.e. of weather data describing arelevant weather situation that actually occurred.

RPS _(forecasted)=∫(CDF _(forecasted)(x)−CDF _(actual)(x))² dx  (3)

In FIGS. 6 a and 6 b, the reference numeral 60 denotes the actualrealization, the reference numeral 61 denotes the cumulativedistribution function CDF_(reference) of the reference weather data, andthe reference numeral 62 denotes the cumulative distribution functionCDF_(forecasted) of the forecasted weather data derived according to thetercile method. In FIG. 6 a, the reference numeral 63 denotes an arearepresenting the ranked probability score for the reference weatherdata. In FIG. 6 b, the reference numeral 64 denotes an area representingthe ranked probability score for the forecasted weather data derivedaccording to the tercile method. Clearly, the tercile method producesresults closer to the actual realization.

In step S32, a ranked probability score (RPS_(reference)) is calculatedfor the reference weather data according to formula (4), whereinCDF_(reference) is the cumulative distribution function of the referenceweather data.

RPS _(reference)=∫(CDF _(reference)(x)−CDF _(actual)(x))² dx  (4)

In step S33, the quality indicator is calculated as a ranked probabilityskill score (RPSS) calculated from the average ranked probability scorefor the forecasted weather data and the average ranked probability scorefor the reference weather data for several years according to formula(5).

RPSS =1− RPS _(forecasted) / RPS _(reference)   (5)

The ranked probability skill score indicates the accuracy of theforecast of the weather data compared to the reference weather data. Theranked probability skill score indicates the percentage of improvementin accuracy of the forecast over the reference simulation. The skillscore has a value of 0% for a forecast with accuracy equal to that ofthe reference, derived solely through statistical simulation. Positivescores indicate that the forecast accuracy is an improvement over thatof the reference. Negative scores indicate that the forecast accuracy isworse than that of the reference. Note that the calculation of thecalculation of the quality indicator is not restricted to the rankedprobability skill score only. Alternative quality measures would workwith the presented methodology as well.

In step S34, the quality indicator is displayed as output on display 3.

In a preferred option, in step S4, the value forecasting module 14calculates the forecasted value of the structured financial product fromthe reference value, calculated in step S2, and from the forecastedvalue, calculated in step S1. The forecasted value of the structuredfinancial product is calculated from the reference value and theforecasted value using the quality indicator, calculated in step S3, asa weighting factor. The forecasted value V_(forecasted) of thestructured financial product is calculated according to formula (6), forexample, wherein a(s) is a function of the quality indicator,V_(forecast) is the forecasted valued calculated in step S1, andV_(reference) is the reference value calculated in step S2.

V _(forecasted) =a( s)·V _(forecast)+(1−a(s))·V _(reference)  (6)

In step S41, the (weighted) forecasted value V_(forecasted) of thestructured financial product is displayed as output on display 3.

It must be pointed out that different sequences of steps are possiblewithout deviating from the scope of the invention. For example, step S32may be performed prior to step S31.

1. A computer implemented method for forecasting a value of aweather-based structured financial product, comprising: calculating aforecast value based on forecasted weather data for a defined timeperiod and a defined geographical area; calculating reference weatherdata from historical weather data for the defined time period and thedefined geographical area; and calculating a quality indicator,indicative of a forecasting quality associated with the forecastedweather data, based on the forecasted weather data and the referenceweather data.
 2. The method according to claim 1, wherein the methodfurther includes calculating a reference value based on the referenceweather data; and wherein the value of the financial product iscalculated from the reference value and from the forecast value weightedby the quality indicator.
 3. The method according to claim 1, whereinthe forecast value is calculated by applying structural parameters ofthe financial product to a forecasted weather index determined from theforecasted weather data; and wherein the reference value is calculatedby applying the structural parameters of the financial product to areference weather index determined from the reference weather data. 4.The method according to claim 3, wherein the forecasted weather data,the reference weather data, and the historical weather data includetemperature data; and wherein the forecasted weather index and thereference weather index include one of average temperature, cumulativetemperature, number of heating degree days, and number of cooling degreedays for the defined time period and the defined geographical area. 5.The method according to claim 1, wherein calculating the qualityindicator includes calculating a ranked probability score for theforecasted weather data, calculating a ranked probability score for thereference weather data, and calculating the quality indicator as aranked probability skill score from the ranked probability score for theforecasted weather data and the ranked probability score for thereference weather data.
 6. The method according to claim 1, wherein theforecasted weather data is calculated from multi-year historical weatherdata and from long-term weather forecast data coveting one or moremonths.
 7. The method according to claim 6, wherein calculating theforecasted weather data includes determining for the defined time perioda first cumulated distribution function for the historical weather data,calculating cumulative values included in terciles of the firstcumulated distribution function, and determining for the defined timeperiod a second cumulated distribution function for the forecastedweather data by downscaling the first cumulated distribution functionusing quantile levels obtained from the long term weather forecast datafor the cumulative values included in the terciles of the firstcumulated distribution function.
 8. The method according to claim 6,wherein calculating the forecasted weather data includes determining forthe defined time period a reference climatology comprising deterministiccomponents from the historical weather data, and calculating theforecasted weather data from the reference climatology and from ensembleforecasts for the defined time period.
 9. The method according to claim1, wherein multiple sets of forecasted weather data for subsequent timeperiods are stored assigned to their respective time period; and whereinthe forecasted weather data is calculated from the multiple sets offorecasted weather data, each set of forecasted weather data beingweighted by a weighting factor having a value increasing from one timeperiod to a subsequent time period.
 10. The method according to claim 1,wherein the forecasted weather data includes a first cumulativedistribution function of temperature data determined from multi-yearhistorical temperature data and from long term temperature forecast datacovering one or more months; wherein calculating the reference weatherdata includes determining a second cumulative distribution function oftemperature data by applying a stochastic time series model to thehistorical temperature data; wherein the forecast value is calculated byapplying structural parameters of the financial product to a forecastedweather index determined from the first cumulative distributionfunction; wherein the reference value is calculated by applyingstructural parameters of the financial product to a reference weatherindex determined from the second cumulative distribution function;wherein calculating the quality indicator includes calculating a firstranked probability score based on the first cumulative distributionfunction, calculating a second ranked probability score based on thesecond cumulative distribution function, and calculating the qualityindicator as a ranked probability skill score from the first rankedprobability score and the second ranked probability score; and whereinthe value of the financial product is calculated from the referencevalue and from the forecast value weighted by the quality indicator. 11.A computer system for forecasting a value of a weather-based structuredfinancial product, comprising: a value forecasting module forcalculating a forecast value based on forecasted weather data for adefined time period and a defined geographical area; a reference modulefor calculating reference weather data from historical weather data forthe defined time period and the defined geographical area; and a qualityindicator module for calculating a quality indicator, indicative of aforecasting quality associated with the forecasted weather data, basedon the forecasted weather data and the reference weather data.
 12. Thecomputer system according to claim I 1, wherein the reference module isconfigured to calculate a reference value based on the reference weatherdata; and wherein the value forecasting module is configured tocalculate the value of the financial product from the reference valueand from the forecast value weighted by the quality indicator.
 13. Thecomputer system according to claim 11, wherein the value forecastingmodule is configured to calculate the forecast value by applyingstructural parameters of the financial product to a forecasted weatherindex determined from the forecasted weather data; and wherein thereference module is configured to calculate the reference value byapplying the structural parameters of the financial product to areference weather index determined from the reference weather data. 14.The computer system according to claim 13, wherein the forecastedweather data, the reference weather data, and the historical weatherdata include temperature data; and wherein the forecasted weather indexand the reference weather index include one of average temperature,cumulative temperature, number of heating degree days, and number ofcooling degree days for the defined time period and the definedgeographical area.
 15. The computer system according to claim 11,wherein the quality indicator module is configured to calculate a rankedprobability score for the forecasted weather data, to calculate a rankedprobability score for the reference weather data, and to calculate thequality indicator as a ranked probability skill score from the rankedprobability score for the forecasted weather data and the rankedprobability score for the reference weather data.
 16. The computersystem according to claim 11, wherein the system includes a weatherforecast module for calculating the forecasted weather data frommulti-year historical weather data and from long-term weather forecastdata covering one or more months.
 17. The computer system according toclaim 16, wherein the weather forecast module is configured to determinefor the defined time period a first cumulated distribution function forthe historical weather data, to calculate cumulative values included interciles of the first cumulated distribution function, and to determinefor the defined time period a second cumulated distribution function forthe forecasted weather data, by downscaling the first cumulateddistribution function using quartile levels, obtained from the long termweather forecast data, for the cumulative values included in theterciles of the first cumulated distribution function.
 18. The computersystem according to claim 16, wherein the weather forecast module isconfigured to determine for the defined time period a referenceclimatology comprising deterministic components from the historicalweather data, and to calculate the forecasted weather data from thereference climatology and from ensemble forecasts for the defined timeperiod.
 19. The computer system according to claim 11, wherein thesystem includes a database with multiple sets of forecasted weather datafor subsequent time periods, each set stored assigned to its respectivetime period; and wherein the system includes a weather forecast modulefor calculating the forecasted weather data from the multiple sets offorecasted weather data, each set of forecasted weather data beingweighted by a weighting factor having a value increasing from one timeperiod to a subsequent time period.
 20. The computer system according toclaim 11, wherein the forecasted weather data includes a firstcumulative distribution function of temperature data determined frommulti-year historical temperature data and from long term temperatureforecast data covering one or more months; wherein the system includes aweather reference module for calculating the reference weather data, theweather reference module being configured to determine a secondcumulative distribution function of temperature data by applying astochastic time series model to the historical temperature data; whereinthe value forecasting module is configured to calculate the forecastvalue by applying structural parameters of the financial product to aforecasted weather index determined from the first cumulativedistribution function; wherein the reference module is configured tocalculate the reference value by applying structural parameters of thefinancial product to a reference weather index, determined from thesecond cumulative distribution function; wherein the quality indicatormodule is configured to calculate a first ranked probability score basedon the first cumulative distribution function, to calculate a secondranked probability score based on the second cumulative distributionfunction, and to calculate the quality indicator as a ranked probabilityskill score from the first ranked probability score and the secondranked probability score; and wherein the value forecasting module isconfigured to calculate the value of the financial product from thereference value and from the forecast value weighted by the qualityindicator.
 21. A computer program product comprising computer programcode means for controlling one or more processors of a computer, suchthat the computer calculates a forecast value based on forecastedweather data for a defined time period and a defined geographical area;calculates reference weather data from historical weather data for thedefined time period and the defined geographical area; and calculates aquality indicator, indicative of a forecasting quality associated withthe forecasted weather data, based on the forecasted weather data andthe reference weather data.
 22. The computer program product accordingto claim 21, comprising further computer program code means forcontrolling the processors of the computer, such that the computercalculates a reference value based on the reference weather data, andcalculates the value of the financial product from the reference valueand from the forecast value weighted by the quality indicator.
 23. Thecomputer program product according to claim 21, comprising furthercomputer program code means for controlling the processors of thecomputer, such that the computer calculates the forecast value byapplying structural parameters of the financial product to a forecastedweather index, determined from the forecasted weather data, and that thecomputer calculates the reference value by applying the structuralparameters of the financial product to a reference weather index,determined from the reference weather data.
 24. The computer programproduct according to claim 23, comprising further computer program codemeans for controlling the processors of the computer, such that theforecasted weather data, the reference weather data, and the historicalweather data include temperature data; and such that the forecastedweather index and the reference weather index include one of averagetemperature, cumulative temperature, number of heating degree days, andnumber of cooling degree days for the defined time period and thedefined geographical area.
 25. The computer program product according toclaim 21, comprising further computer program code means for controllingthe processors of the computer, such that the computer calculates aranked probability score for the forecasted weather data, calculates aranked probability score for the reference weather data, and calculatesthe quality indicator as a ranked probability skill score from theranked probability score for the forecasted weather data and the rankedprobability score for the reference weather data.
 26. The computerprogram product according to claim 21, comprising further computerprogram code means for controlling the processors of the computer, suchthat the computer calculates the forecasted weather data from multi-yearhistorical weather data and from long-term weather forecast datecovering one or more months.
 27. The computer program product accordingto claim 26, comprising further computer program code means forcontrolling the processors of the computer, such that the computerdetermines for the defined time period a first cumulated distributionfunction for the historical weather data, calculates cumulative valuesincluded in terciles of the first cumulated distribution function, anddetermines for the defined time period a second cumulated distributionfunction for the forecasted weather data by downscaling the firstcumulated distribution function using quantile levels, obtained from thelong term weather forecast data, for the cumulative values included inthe terciles of the first cumulated distribution function.
 28. Thecomputer program product according to claim 26, comprising furthercomputer program code means for controlling the processors of thecomputer, such that the computer determines for the defined time perioda reference climatology comprising deterministic components from thehistorical weather data, and calculates the forecasted weather data fromthe reference climatology and from ensemble forecasts for the definedtime period.
 29. The computer program product according to claim 21,comprising further computer program code means for controlling theprocessors of the computer, such that the computer stores multiple setsof forecasted weather data for subsequent time periods, each set beingassigned to its respective time period, and calculates the forecastedweather data from the multiple sets of forecasted weather data, each setof forecasted weather data being weighted by a weighting factor having avalue increasing from one time period to a subsequent time period. 30.The computer program product according to claim 21, comprising furthercomputer program code means for controlling the processors of thecomputer, such that the forecasted weather data includes a firstcumulative distribution function of temperature data determined frommulti-year historical temperature data and from long term temperatureforecast data covering one or more months; and such that the computerdetermines a second cumulative distribution function of temperature databy applying a stochastic time series model to the historical temperaturedata, that the computer calculates the forecast value by applyingstructural parameters of the financial product to a forecasted weatherindex, determined from the first cumulative distribution function, thatthe computer calculates the reference value by applying structuralparameters of the financial product to a reference weather index,determined from the second cumulative distribution function, that thecomputer calculates a first ranked probability score based on the firstcumulative distribution function, that the computer calculates a secondranked probability score based on the second cumulative distributionfunction, that the computer calculates the quality indicator as a rankedprobability skill score from the first ranked probability score and thesecond ranked probability score, and that the computer calculates thevalue of the financial product from the reference value and from theforecast value weighted by the quality indicator.